471 lines
18 KiB
Python
471 lines
18 KiB
Python
# Copyright (c) Meta Platforms, Inc. and affiliates.
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# All rights reserved.
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# This source code is licensed under the license found in the
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# LICENSE file in the root directory of this source tree.
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import warnings
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import numpy as np
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import cv2
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import torch
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import torch.nn.functional as F
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from cotracker.models.core.model_utils import smart_cat, get_points_on_a_grid
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from cotracker.models.build_cotracker import build_cotracker
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def gen_gaussian_heatmap(imgSize=200):
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circle_img = np.zeros((imgSize, imgSize), np.float32)
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circle_mask = cv2.circle(circle_img, (imgSize//2, imgSize//2), imgSize//2, 1, -1)
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isotropicGrayscaleImage = np.zeros((imgSize, imgSize), np.float32)
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# Guass Map
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for i in range(imgSize):
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for j in range(imgSize):
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isotropicGrayscaleImage[i, j] = 1 / 2 / np.pi / (40 ** 2) * np.exp(
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-1 / 2 * ((i - imgSize / 2) ** 2 / (40 ** 2) + (j - imgSize / 2) ** 2 / (40 ** 2)))
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isotropicGrayscaleImage = isotropicGrayscaleImage * circle_mask
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isotropicGrayscaleImage = (isotropicGrayscaleImage / np.max(isotropicGrayscaleImage)).astype(np.float32)
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isotropicGrayscaleImage = (isotropicGrayscaleImage / np.max(isotropicGrayscaleImage)*255).astype(np.uint8)
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# isotropicGrayscaleImage = cv2.resize(isotropicGrayscaleImage, (40, 40))
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return isotropicGrayscaleImage
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def draw_heatmap(img, center_coordinate, heatmap_template, side, width, height):
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x1 = max(center_coordinate[0] - side, 1)
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x2 = min(center_coordinate[0] + side, width - 1)
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y1 = max(center_coordinate[1] - side, 1)
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y2 = min(center_coordinate[1] + side, height - 1)
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x1, x2, y1, y2 = int(x1), int(x2), int(y1), int(y2)
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if (x2 - x1) < 1 or (y2 - y1) < 1:
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print(center_coordinate, "x1, x2, y1, y2", x1, x2, y1, y2)
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return img
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need_map = cv2.resize(heatmap_template, (x2-x1, y2-y1))
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img[y1:y2,x1:x2] = need_map
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return img
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def generate_gassian_heatmap(pred_tracks, pred_visibility=None, image_size=None, side=20):
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width, height = image_size
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num_frames, num_points = pred_tracks.shape[:2]
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point_index_list = [point_idx for point_idx in range(num_points)]
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heatmap_template = gen_gaussian_heatmap()
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image_list = []
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for frame_idx in range(num_frames):
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img = np.zeros((height, width), np.float32)
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for point_idx in point_index_list:
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px, py = pred_tracks[frame_idx, point_idx]
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if px < 0 or py < 0 or px >= width or py >= height:
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continue
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if pred_visibility is not None:
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if (not pred_visibility[frame_idx, point_idx]):
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continue
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img = draw_heatmap(img, (px, py), heatmap_template, side, width, height)
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img = cv2.cvtColor(img.astype(np.uint8), cv2.COLOR_GRAY2RGB)
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img = torch.from_numpy(img).permute(2, 0, 1).contiguous()
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image_list.append(img)
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video_gaussion_map = torch.stack(image_list, dim=0)
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return video_gaussion_map
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# TODO: need further check and investigation
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def sample_trajectories(
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pred_tracks, pred_visibility,
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max_points=10,
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motion_threshold=1,
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vis_threshold=5,
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):
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# pred_tracks: (b, f, num_points, 2)
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# pred_visibility: (b, f, num_points)
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batch_size, num_frames, num_points = pred_visibility.shape
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# 1. Remove points with low visibility
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mask = pred_visibility.sum(dim=1) > vis_threshold
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mask = mask.unsqueeze(1).repeat(1, num_frames, 1)
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pred_tracks = pred_tracks[mask].view(batch_size, num_frames, -1, 2)
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pred_visibility = pred_visibility[mask].view(batch_size, num_frames, -1)
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# 2. Thresholding: remove points with too small motions
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# compute the motion of each point
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diff = pred_tracks[:, 1:] - pred_tracks[:, :-1]
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# (b, f-1, num_points), sqrt(x^2 + y^2)
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motion = torch.norm(diff, dim=-1)
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# (b, num_points), mean motion for each point
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motion = torch.mean(motion, dim=1)
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# apply threshold
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mask = motion > motion_threshold # (b, num_points)
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assert mask.shape[0] == 1
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num_keeped = mask.sum()
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if num_keeped < max_points:
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indices = torch.argsort(motion, dim=-1, descending=True)[:, :max_points] # (bs, max_points)
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mask = torch.zeros_like(mask) # (bs, num_points)
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# set mask to 1 for the top max_points
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mask[0, indices] = 1
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num_keeped = mask.sum() # note sometimes mask.sum() < max_points
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motion = motion[mask].view(batch_size, num_keeped)
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# keep shape
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mask = mask.unsqueeze(1).repeat(1, num_frames, 1)
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pred_tracks = pred_tracks[mask].view(batch_size, num_frames, num_keeped, 2)
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pred_visibility = pred_visibility[mask].view(batch_size, num_frames, num_keeped)
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# 3. Sampling with larger prob for large motions
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num_points = min(max_points, num_keeped)
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if num_points == 0:
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warnings.warn("No points left after filtering")
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return None, None
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prob = motion / motion.max()
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prob = prob / prob.sum()
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sampled_indices = torch.multinomial(prob, num_points, replacement=False)
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sampled_indices = sampled_indices.squeeze(0) # (num_points, )
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pred_tracks_sampled = pred_tracks[:, :, sampled_indices]
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pred_visibility_sampled = pred_visibility[:, :, sampled_indices]
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return pred_tracks_sampled, pred_visibility_sampled
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def sample_trajectories_with_ref(
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pred_tracks, pred_visibility, coords0,
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max_points=10,
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motion_threshold=1,
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vis_threshold=5,
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):
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batch_size, num_frames, num_points = pred_visibility.shape
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visibility_sum = pred_visibility.sum(dim=1)
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vis_mask = visibility_sum > vis_threshold # (batch_size, num_points)
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pred_tracks = pred_tracks * vis_mask.unsqueeze(1).unsqueeze(-1) # (batch_size, num_frames, num_points, 2)
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pred_visibility = pred_visibility * vis_mask.unsqueeze(1)
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indices = vis_mask.nonzero(as_tuple=False) # (num_visible_points, 2)
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if indices.size(0) == 0:
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warnings.warn("No points left after visibility filtering")
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return None, None, None
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batch_indices, point_indices = indices[:, 0], indices[:, 1]
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coords0_filtered = coords0[batch_indices, point_indices] # (num_visible_points, 2)
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diff = pred_tracks[:, 1:] - pred_tracks[:, :-1] # (batch_size, num_frames-1, num_points, 2)
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motion = torch.norm(diff, dim=-1).mean(dim=1) # (batch_size, num_points)
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motion_mask = motion > motion_threshold
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combined_mask = vis_mask & motion_mask # (batch_size, num_points)
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indices = combined_mask.nonzero(as_tuple=False)
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if indices.size(0) == 0:
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warnings.warn("No points left after motion filtering")
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return None, None, None
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batch_indices, point_indices = indices[:, 0], indices[:, 1]
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pred_tracks_filtered = pred_tracks[batch_indices, :, point_indices, :] # (num_filtered_points, num_frames, 2)
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pred_visibility_filtered = pred_visibility[batch_indices, :, point_indices] # (num_filtered_points, num_frames)
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coords0_filtered = coords0[batch_indices, point_indices, :] # (num_filtered_points, 2)
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motion_filtered = motion[batch_indices, point_indices] # (num_filtered_points)
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num_keeped = motion_filtered.size(0)
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num_points_sampled = min(max_points, num_keeped)
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if num_points_sampled == 0:
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warnings.warn("No points left after filtering")
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return None, None, None
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prob = motion_filtered / motion_filtered.max()
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prob = prob / prob.sum()
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sampled_indices = torch.multinomial(prob, num_points_sampled, replacement=False)
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pred_tracks_sampled = pred_tracks_filtered[sampled_indices] # (num_points_sampled, num_frames, 2)
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pred_visibility_sampled = pred_visibility_filtered[sampled_indices] # (num_points_sampled, num_frames)
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coords0_sampled = coords0_filtered[sampled_indices] # (num_points_sampled, 2)
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pred_tracks_sampled = pred_tracks_sampled.view(batch_size, num_points_sampled, num_frames, 2).transpose(1, 2)
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pred_visibility_sampled = pred_visibility_sampled.view(batch_size, num_points_sampled, num_frames).transpose(1, 2)
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coords0_sampled = coords0_sampled.view(batch_size, num_points_sampled, 2)
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return pred_tracks_sampled, pred_visibility_sampled, coords0_sampled
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class CoTrackerPredictor(torch.nn.Module):
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def __init__(
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self,
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checkpoint="./checkpoints/cotracker2.pth",
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shift_grid=False,
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):
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super().__init__()
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self.support_grid_size = 6
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model = build_cotracker(checkpoint)
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self.interp_shape = model.model_resolution
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self.model = model
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self.model.eval()
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self.shift_grid = shift_grid
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@torch.no_grad()
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def forward(
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self,
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video, # (B, T, 3, H, W)
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# input prompt types:
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# - None. Dense tracks are computed in this case. You can adjust *query_frame* to compute tracks starting from a specific frame.
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# *backward_tracking=True* will compute tracks in both directions.
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# - queries. Queried points of shape (B, N, 3) in format (t, x, y) for frame index and pixel coordinates.
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# - grid_size. Grid of N*N points from the first frame. if segm_mask is provided, then computed only for the mask.
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# You can adjust *query_frame* and *backward_tracking* for the regular grid in the same way as for dense tracks.
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queries: torch.Tensor = None,
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segm_mask: torch.Tensor = None, # Segmentation mask of shape (B, 1, H, W)
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grid_size: int = 0,
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grid_query_frame: int = 0, # only for dense and regular grid tracks
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backward_tracking: bool = False,
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):
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if queries is None and grid_size == 0:
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tracks, visibilities = self._compute_dense_tracks(
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video,
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grid_query_frame=grid_query_frame,
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backward_tracking=backward_tracking,
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)
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else:
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tracks, visibilities = self._compute_sparse_tracks(
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video,
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queries,
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segm_mask,
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grid_size,
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add_support_grid=(grid_size == 0 or segm_mask is not None),
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grid_query_frame=grid_query_frame,
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backward_tracking=backward_tracking,
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)
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return tracks, visibilities
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def _compute_dense_tracks(self, video, grid_query_frame, grid_size=80, backward_tracking=False):
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*_, H, W = video.shape
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grid_step = W // grid_size
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grid_width = W // grid_step
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grid_height = H // grid_step
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tracks = visibilities = None
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grid_pts = torch.zeros((1, grid_width * grid_height, 3)).to(video.device)
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grid_pts[0, :, 0] = grid_query_frame
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for offset in range(grid_step * grid_step):
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print(f"step {offset} / {grid_step * grid_step}")
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ox = offset % grid_step
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oy = offset // grid_step
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grid_pts[0, :, 1] = torch.arange(grid_width).repeat(grid_height) * grid_step + ox
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grid_pts[0, :, 2] = (
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torch.arange(grid_height).repeat_interleave(grid_width) * grid_step + oy
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)
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tracks_step, visibilities_step = self._compute_sparse_tracks(
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video=video,
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queries=grid_pts,
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backward_tracking=backward_tracking,
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)
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tracks = smart_cat(tracks, tracks_step, dim=2)
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visibilities = smart_cat(visibilities, visibilities_step, dim=2)
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return tracks, visibilities
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def _compute_sparse_tracks(
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self,
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video,
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queries,
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segm_mask=None,
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grid_size=0,
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add_support_grid=False,
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grid_query_frame=0,
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backward_tracking=False,
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):
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B, T, C, H, W = video.shape
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video = video.reshape(B * T, C, H, W)
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video = F.interpolate(video, tuple(self.interp_shape), mode="bilinear", align_corners=True)
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video = video.reshape(B, T, 3, self.interp_shape[0], self.interp_shape[1])
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if queries is not None:
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B, N, D = queries.shape
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assert D == 3
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queries = queries.clone()
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queries[:, :, 1:] *= queries.new_tensor(
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[
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(self.interp_shape[1] - 1) / (W - 1),
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(self.interp_shape[0] - 1) / (H - 1),
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]
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)
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elif grid_size > 0:
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grid_pts = get_points_on_a_grid(grid_size, self.interp_shape, device=video.device, shift_grid=self.shift_grid)
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if segm_mask is not None:
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segm_mask = F.interpolate(segm_mask, tuple(self.interp_shape), mode="nearest")
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point_mask = segm_mask[0, 0][
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(grid_pts[0, :, 1]).round().long().cpu(),
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(grid_pts[0, :, 0]).round().long().cpu(),
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].bool()
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grid_pts = grid_pts[:, point_mask]
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queries = torch.cat(
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[torch.ones_like(grid_pts[:, :, :1]) * grid_query_frame, grid_pts],
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dim=2,
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).repeat(B, 1, 1)
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if add_support_grid:
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grid_pts = get_points_on_a_grid(
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self.support_grid_size, self.interp_shape, device=video.device, shift_grid=self.shift_grid,
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)
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grid_pts = torch.cat([torch.zeros_like(grid_pts[:, :, :1]), grid_pts], dim=2)
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grid_pts = grid_pts.repeat(B, 1, 1)
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queries = torch.cat([queries, grid_pts], dim=1)
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tracks, visibilities, __ = self.model.forward(video=video, queries=queries, iters=6)
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if backward_tracking:
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tracks, visibilities = self._compute_backward_tracks(
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video, queries, tracks, visibilities
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)
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if add_support_grid:
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queries[:, -self.support_grid_size**2 :, 0] = T - 1
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if add_support_grid:
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tracks = tracks[:, :, : -self.support_grid_size**2]
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visibilities = visibilities[:, :, : -self.support_grid_size**2]
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thr = 0.9
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visibilities = visibilities > thr
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# correct query-point predictions
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# see https://github.com/facebookresearch/co-tracker/issues/28
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# TODO: batchify
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for i in range(len(queries)):
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queries_t = queries[i, : tracks.size(2), 0].to(torch.int64)
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arange = torch.arange(0, len(queries_t))
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# overwrite the predictions with the query points
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tracks[i, queries_t, arange] = queries[i, : tracks.size(2), 1:]
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# correct visibilities, the query points should be visible
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visibilities[i, queries_t, arange] = True
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tracks *= tracks.new_tensor(
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[(W - 1) / (self.interp_shape[1] - 1), (H - 1) / (self.interp_shape[0] - 1)]
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)
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return tracks, visibilities
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def _compute_backward_tracks(self, video, queries, tracks, visibilities):
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inv_video = video.flip(1).clone()
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inv_queries = queries.clone()
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inv_queries[:, :, 0] = inv_video.shape[1] - inv_queries[:, :, 0] - 1
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inv_tracks, inv_visibilities, __ = self.model(video=inv_video, queries=inv_queries, iters=6)
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inv_tracks = inv_tracks.flip(1)
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inv_visibilities = inv_visibilities.flip(1)
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arange = torch.arange(video.shape[1], device=queries.device)[None, :, None]
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mask = (arange < queries[:, None, :, 0]).unsqueeze(-1).repeat(1, 1, 1, 2)
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tracks[mask] = inv_tracks[mask]
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visibilities[mask[:, :, :, 0]] = inv_visibilities[mask[:, :, :, 0]]
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return tracks, visibilities
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class CoTrackerOnlinePredictor(torch.nn.Module):
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def __init__(self, checkpoint="./checkpoints/cotracker2.pth"):
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super().__init__()
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self.support_grid_size = 6
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model = build_cotracker(checkpoint)
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self.interp_shape = model.model_resolution
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self.step = model.window_len // 2
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self.model = model
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self.model.eval()
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@torch.no_grad()
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def forward(
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self,
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video_chunk,
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is_first_step: bool = False,
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queries: torch.Tensor = None,
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grid_size: int = 10,
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grid_query_frame: int = 0,
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add_support_grid=False,
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):
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B, T, C, H, W = video_chunk.shape
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# Initialize online video processing and save queried points
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# This needs to be done before processing *each new video*
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if is_first_step:
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self.model.init_video_online_processing()
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if queries is not None:
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B, N, D = queries.shape
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assert D == 3
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queries = queries.clone()
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queries[:, :, 1:] *= queries.new_tensor(
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[
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(self.interp_shape[1] - 1) / (W - 1),
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(self.interp_shape[0] - 1) / (H - 1),
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]
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)
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elif grid_size > 0:
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grid_pts = get_points_on_a_grid(
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grid_size, self.interp_shape, device=video_chunk.device
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)
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queries = torch.cat(
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[torch.ones_like(grid_pts[:, :, :1]) * grid_query_frame, grid_pts],
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dim=2,
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)
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if add_support_grid:
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grid_pts = get_points_on_a_grid(
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self.support_grid_size, self.interp_shape, device=video_chunk.device
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)
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grid_pts = torch.cat([torch.zeros_like(grid_pts[:, :, :1]), grid_pts], dim=2)
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queries = torch.cat([queries, grid_pts], dim=1)
|
|
self.queries = queries
|
|
return (None, None)
|
|
|
|
video_chunk = video_chunk.reshape(B * T, C, H, W)
|
|
video_chunk = F.interpolate(
|
|
video_chunk, tuple(self.interp_shape), mode="bilinear", align_corners=True
|
|
)
|
|
video_chunk = video_chunk.reshape(B, T, 3, self.interp_shape[0], self.interp_shape[1])
|
|
|
|
tracks, visibilities, __ = self.model(
|
|
video=video_chunk,
|
|
queries=self.queries,
|
|
iters=6,
|
|
is_online=True,
|
|
)
|
|
thr = 0.9
|
|
return (
|
|
tracks
|
|
* tracks.new_tensor(
|
|
[
|
|
(W - 1) / (self.interp_shape[1] - 1),
|
|
(H - 1) / (self.interp_shape[0] - 1),
|
|
]
|
|
),
|
|
visibilities > thr,
|
|
)
|